ABSTRACT Assessing dietary intake in children is difficult and limited validated tools exist. Plasma carotenoids are nutritional biomarkers of fruit and vegetable intake and therefore suitable to validate reported dietary intakes. The aim of this study was to examine the comparative validity of a food frequency questionnaire (FFQ), completed by parents reporting child fruit and vegetable intake compared to plasma carotenoid concentrations. A sample of children aged 5-12 years (n = 93) from a range of weight categories were assessed. Dietary intake was measured using a 137-item semi-quantitative FFQ. Plasma carotenoids were measured using reverse phase high-performance liquid chromatography. Pearson correlation coefficients between reported dietary intake of carotenoids and plasma carotenoid concentrations were strongest after adjustment for BMI (beta-carotene (r = 0.56, P < 0.05), alpha-carotene (r = 0.51, P < 0.001), cryptoxanthin (r = 0.32, P < 0.001)). Significantly lower levels (P < 0.05) of all plasma carotenoids, except lutein, were found among overweight and obese children when compared to healthy weight children. Parental report of children's carotenoid intakes, using a FFQ can be used to provide a relative validation of fruit and vegetable intake. The lower plasma carotenoid concentrations found in overweight and obese children requires further investigation.

[Show abstract][Hide abstract]ABSTRACT: Background: Lack of uniformity in outcome measures used in evaluations of childhood obesity treatment interventions can impede the ability to assess effectiveness and limits comparisons across trials. Lack of uniformity in outcome measures used in evaluations of childhood obesity treatment interventions can impede the ability to assess effectiveness and limits comparisons across trials. Objective: To identify and appraise outcome measures to produce a framework of recommended measures for use in evaluations of childhood obesity treatment interventions. To identify and appraise outcome measures to produce a framework of recommended measures for use in evaluations of childhood obesity treatment interventions. Data sources: Eleven electronic databases were searched between August and December 2011, including MEDLINE; MEDLINE In-Process and Other Non-Indexed Citations; EMBASE; PsycINFO; Health Management Information Consortium (HMIC); Allied and Complementary Medicine Database (AMED); Global Health, Maternity and Infant Care (all Ovid); Cumulative Index to Nursing and Allied Health Literature (CINAHL) (EBSCOhost); Science Citation Index (SCI) [Web of Science (WoS)]; and The Cochrane Library (Wiley) - from the date of inception, with no language restrictions. This was supported by review of relevant grey literature and trial databases. Eleven electronic databases were searched between August and December 2011, including MEDLINE; MEDLINE In-Process and Other Non-Indexed Citations; EMBASE; PsycINFO; Health Management Information Consortium (HMIC); Allied and Complementary Medicine Database (AMED); Global Health, Maternity and Infant Care (all Ovid); Cumulative Index to Nursing and Allied Health Literature (CINAHL) (EBSCOhost); Science Citation Index (SCI) [Web of Science (WoS)]; and The Cochrane Library (Wiley) - from the date of inception, with no language restrictions. This was supported by review of relevant grey literature and trial databases. Review methods: Two searches were conducted to identify (1) outcome measures and corresponding citations used in published childhood obesity treatment evaluations and (2) manuscripts describing the development and/or evaluation of the outcome measures used in the childhood intervention obesity evaluations. Search 1 search strategy (review of trials) was modelled on elements of a review by Luttikhuis et al. (Oude Luttikhuis H, Baur L, Jansen H, Shrewsbury VA, O'Malley C, Stolk RP, et al. Interventions for treating obesity in children. Cochrane Database Syst Rev 2009;1:CD001872). Search 2 strategy (methodology papers) was built on Terwee et al.'s search filter (Terwee CB, Jansma EP, Riphagen II, de Vet HCW. Development of a methodological PubMed search filter for finding studies on measurement properties of measurement instruments. Qual Life Res 2009;18:1115-23). Eligible papers were appraised for quality initially by the internal project team. This was followed by an external appraisal by expert collaborators in order to agree which outcome measures should be recommended for the Childhood obesity Outcomes Review (CoOR) outcome measures framework. Two searches were conducted to identify (1) outcome measures and corresponding citations used in published childhood obesity treatment evaluations and (2) manuscripts describing the development and/or evaluation of the outcome measures used in the childhood intervention obesity evaluations. Search 1 search strategy (review of trials) was modelled on elements of a review by Luttikhuis et al. (Oude Luttikhuis H, Baur L, Jansen H, Shrewsbury VA, O'Malley C, Stolk RP, et al. Interventions for treating obesity in children. Cochrane Database Syst Rev 2009;1:CD001872). Search 2 strategy (methodology papers) was built on Terwee et al.'s search filter (Terwee CB, Jansma EP, Riphagen II, de Vet HCW. Development of a methodological PubMed search filter for finding studies on measurement properties of measurement instruments. Qual Life Res 2009;18:1115-23). Eligible papers were appraised for quality initially by the internal project team. This was f llowed by an external appraisal by expert collaborators in order to agree which outcome measures should be recommended for the Childhood obesity Outcomes Review (CoOR) outcome measures framework. Results: Three hundred and seventy-nine manuscripts describing 180 outcome measures met eligibility criteria. Appraisal of these resulted in the recommendation of 36 measures for the CoOR outcome measures framework. Recommended primary outcome measures were body mass index (BMI) and dual-energy X-ray absorptiometry (DXA). Experts did not advocate any self-reported measures where objective measurement was possible (e.g. physical activity). Physiological outcomes hold potential to be primary outcomes, as they are indicators of cardiovascular health, but without evidence of what constitutes a minimally importance difference they have remained as secondary outcomes (although the corresponding lack of evidence for BMI and DXA is acknowledged). No preference-based quality-of-life measures were identified that would enable economic evaluation via calculation of quality-adjusted life-years. Few measures reported evaluating responsiveness. Three hundred and seventy-nine manuscripts describing 180 outcome measures met eligibility criteria. Appraisal of these resulted in the recommendation of 36 measures for the CoOR outcome measures framework. Recommended primary outcome measures were body mass index (BMI) and dual-energy X-ray absorptiometry (DXA). Experts did not advocate any self-reported measures where objective measurement was possible (e.g. physical activity). Physiological outcomes hold potential to be primary outcomes, as they are indicators of cardiovascular health, but without evidence of what constitutes a minimally importance difference they have remained as secondary outcomes (although the corresponding lack of evidence for BMI and DXA is acknowledged). No preference-based quality-of-life measures were identified that would enable economic evaluation via calculation of quality-adjusted life-years. Few measures reported evaluating responsiveness. Limitations: Proposed recommended measures are fit for use as outcome measures within studies that evaluate childhood obesity treatment evaluations specifically. These may or may not be suitable for other study designs, and some excluded measures may be more suitable in other study designs. Proposed recommended measures are fit for use as outcome measures within studies that evaluate childhood obesity treatment evaluations specifically. These may or may not be suitable for other study designs, and some excluded measures may be more suitable in other study designs. Conclusions: The CoOR outcome measures framework provides clear guidance of recommended primary and secondary outcome measures. This will enhance comparability between treatment evaluations and ensure that appropriate measures are being used. Where possible, future work should focus on modification and evaluation of existing measures rather than development of tools de nova. In addition, it is recommended that a similar outcome measures framework is produced to support evaluation of adult obesity programmes. The CoOR outcome measures framework provides clear guidance of recommended primary and secondary outcome measures. This will enhance comparability between treatment evaluations and ensure that appropriate measures are being used. Where possible, future work should focus on modification and evaluation of existing measures rather than development of tools de nova. In addition, it is recommended that a similar outcome measures framework is produced to support evaluation of adult obesity programmes. Funding: The National Institute for Health Research Health Technology Assessment programme. The National Institute for Health Research Health Technology Assessment programme.

[Show abstract][Hide abstract]ABSTRACT: Lutein and zeaxanthin are xanthophyll carotenoids present in highly pigmented vegetables and fruits. Lutein is selectively accumulated in the brain relative to other carotenoids. Recent evidence has linked lutein to cognition in older adults, but little is known about lutein in young children, despite structural brain development. We determined lutein intake using FFQ, one 24 h recall and three 24 h recalls, plasma lutein concentrations and their association with cognition in 160 children 5·6-5·9 years of age, at low risk for neurodevelopmental delay. Plasma lutein was skewed, with a median of 0·23 (2·5th to 95th percentile range 0·11-0·53) µmol/l. Plasma lutein showed a higher correlation with lutein intake estimated as the average of three 24 h recalls (r 0·479; P = 0·001), rather than one 24 h recall (r 0·242; P = 0·003) or FFQ (r 0·316; P = 0·001). The median lutein intake was 697 (2·5th to 95th percentile range 178-5287) µg/d based on three 24 h recalls. Lutein intake was inversely associated with SFA intake, but dietary fat or SFA intakes were not associated with plasma lutein. No associations were found between plasma lutein or lutein intake and any measure of cognition. While subtle independent effects of lutein on child cognition are possible, separating these effects from covariates making an impact on both child diet and cognition may be difficult.

05/2014; 3:e11. DOI:10.1017/jns.2014.10

This article is viewable in ResearchGate's enriched format

RG Format enables you to read in context with side-by-side figures, citations, and feedback from experts in your field.

Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

Page 1

162 VOLUME 17 NUMBER 1 | JANUARY 2009 | www.obesityjournal.orgarticlesepidemiologynature publishing groupIntroductIonAssessing dietary intake in children is a particularly difficult task due to numerous factors including children’s cognitive ability, and decreased concentration span which influence abil-ity to both recall foods and estimate portions sizes (1). For these reasons dietary intake in children is commonly reported by par-ents or caretakers (1). Efforts have been made to validate paren-tal food recalls (1) and food frequency questionnaires (FFQs) (2) for children <12 years. Overweight in both parents and chil-dren has been shown to affect accuracy (1) with overreporting of foods considered healthy, and underreporting of foods per-ceived as less healthy (3). Limited validated tools exist to assess dietary intake in children under 10 years of age. Validity is the accuracy or precision of a measure. It is assessed by compar-ing results using a “gold standard” measure of known validity to values obtained by another instrument. There is no gold standard measurements in free living individuals for measuring total or individual nutrient intakes within the dietary assess-ment tools or completely objective measures currently avail-able (4). Plasma biomarkers can act as a proxy for specific food intake and are therefore suitable for use in validating dietary assessment tools (5). Although comparison of dietary assess-ment methods is often undertaken, it is an unsuitable method of validation due to the risk of correlated error. Biomarkers offer an objective and independent measure of validation (5). Plasma concentrations of carotenoids are reported to reflect intake of fruits and vegetables due to the abundance of these compounds in these food sources (6) despite individual vari-ability in absorption, availability, and metabolism (6,7). A range of carotenoids were chosen as biomarkers of fruit and vegetable intake, as it has been previously suggested that a single biomar-ker is unlikely to be meaningful because of the diverse phyto-chemical composition of plant foods (8).Previous research has shown a dose–response relationship between intake and appearance in plasma (9), making caroten-oids a reliable biomarker of intake. The most commonly meas-ured carotenoids in studies to date include the provitamin A compounds: α-carotene, β-carotene, cryptoxanthin, and also lycopene and lutein. Validation studies have largely been car-ried out in adults (10,11) and relatively few studies have been conducted in children (2,12,13). However these studies suggest body weight is a confounder, with higher relative weights asso-ciated with lower plasma carotenoid concentrations (14).The aim of this study was to validate parental report of children’s fruit and vegetable intake by FFQ by comparing with a range of plasma carotenoids. A secondary aim was to Validation of Overweight Children’s Fruit and Vegetable Intake Using Plasma CarotenoidsTracy L. Burrows1, Janet M. Warren2, Kim Colyvas3, Manohar L. Garg4 and Clare E. Collins1Assessing dietary intake in children is difficult and limited validated tools exist. Plasma carotenoids are nutritional biomarkers of fruit and vegetable intake and therefore suitable to validate reported dietary intakes. The aim of this study was to examine the comparative validity of a food frequency questionnaire (FFQ), completed by parents reporting child fruit and vegetable intake compared to plasma carotenoid concentrations. A sample of children aged 5–12 years (n = 93) from a range of weight categories were assessed. Dietary intake was measured using a 137-item semi-quantitative FFQ. Plasma carotenoids were measured using reverse phase high-performance liquid chromatography. Pearson correlation coefficients between reported dietary intake of carotenoids and plasma carotenoid concentrations were strongest after adjustment for BMI (β-carotene (r = 0.56, P < 0.05), α-carotene (r = 0.51, P < 0.001), cryptoxanthin (r = 0.32, P < 0.001)). Significantly lower levels (P < 0.05) of all plasma carotenoids, except lutein, were found among overweight and obese children when compared to healthy weight children. Parental report of children’s carotenoid intakes, using a FFQ can be used to provide a relative validation of fruit and vegetable intake. The lower plasma carotenoid concentrations found in overweight and obese children requires further investigation.Obesity (2008) 17, 162–168. doi:10.1038/oby.2008.4951School of Health Sciences, Faculty of Health, University of Newcastle, Newcastle, New South Wales, Australia; 2MRC, Human Nutrition Research, Elsie Widdowson Laboratory, Cambridge, United Kingdom; 3School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, New South Wales, Australia; 4Nutraceuticals Research Group, School of Biomedical Sciences, University of Newcastle, Newcastle, New South Wales, Australia. Correspondence: Tracy L. Burrows (Tracy.Burrows@newcastle.edu.au)Received 29 May 2008; accepted 20 August 2008; published online 6 November 2008. doi:10.1038/oby.2008.495

Page 2

obesity | VOLUME 17 NUMBER 1 | JANUARY 2009 163articlesepidemiologycompare carotenoid concentrations in overweight and healthy weight children.MaterIal and MethodssubjectsThe subjects were a sample of overweight and obese children drawn from the HIKCUPS (Hunter Illawarra Kids Challenge Using Parent Support) study, a multisite randomized controlled trial comparing the efficacy of three intervention groups in the treatment of childhood obes-ity. Full methodological details have been published previously (15). Baseline and 6-month postintervention data were included for 46 chil-dren. The 6-month values were used as separate data points to gener-ate a larger range of plasma carotenoid concentrations. Children were selected if their parents had participated in the nutrition intervention arms, had both the baseline and postintervention blood samples and had reported not taking vitamin supplements.The comparison children (n = 47) were drawn from the same broad age group and location, the Hunter region, NSW, Australia (population ~550,000), but were not specifically matched on other characteristics. Parents of children in this group were invited by mail if they had indi-cated in a previous nutrition survey (16) that they would be interested in being contacted about future research projects. Children were included if parental written consent for child participation was obtained. Ethics approval was obtained from the University of Newcastle. All subjects were recruited and data collected by trained assessors between April 2005 and July 2006.All parents completed a FFQ. Children voluntarily provided a fasting blood sample. A recruitment flow diagram is presented in Figure 1.Weight was measured to the nearest 100 g wearing light clothing using Tanita HD646 scales (Tanita Corporation, IL). Height was measured to the nearest 0.1 cm using the stretch stature method and PE87 port-able stadiometers (Mentone Educational Centre, Victoria, Australia). Nonextensible steel tape measures were used to measure waist circum-ference, which was measured at the level of the midpoint between the lower costal border and the iliac crest. Anthropometry measures were conducted using the International Society for the Advancement of Kinanthropometry (ISAK) procedures (17). BMI z scores were calcu-lated using a computer program (18) based on LMS methods; children were classified into weight categories of healthy, overweight, or obese according to UK BMI z score cut points (19).Dietary intake was measured using The Australian Child and Ado-lescent Eating Survey, a 137-item semi-quantitative FFQ which was for-merly tested for reliability and relative validity. The tool demonstrated acceptable accuracy for ranking children’s nutrient intakes in Australian children aged 9–16 years (20). Portion sizes for individual food items included in the Australian Child and Adolescent Eating Survey FFQ were determined using the “natural” serving size (e.g., slice of bread) or, in the absence of a natural serving size, portion size data from the Australian National Nutrition Survey of children, 1995, were used (21). There were eight items without a “natural” serving size or Australian National Nutri-tion Survey of children data. For these foods, either Food Works nutrient calculation software (22) “Unspecified” serve sizes (five items) or packet serve sizes (three items) were used. Parents were asked about frequency of their child’s consumption of a defined list of foods over the previous 6 months. Twenty-one questions related directly to intake of vegetables and 11 to fruit. Due to the seasonal availability of some fruits, a separate sec-tion was included in the FFQ for seasonal fruit. The frequency categories were listed as for other food items, with the question, “when the following fruit is in season, how often do you usually eat it?” to capture the usual consumption of the fruit when it is in season. Seasonal availability was determined by contacting the food markets, Sydney, NSW and obtaining information about the wider availability in other markets and supermar-kets during the year, in addition to referring to supermarket literature that indicated the months of the year different seasonal fruit was available. The average daily intake for the year was calculated for seasonal fruit as shown below. Average daily intake of seasonal fruit = fm/12, Where; f = the daily frequency of consumption of the fruit when it is in season and m = the number of months each year that the fruit is available. A trained research dietitian explained and administered the self-complete survey.The FFQ was analyzed using primarily the Australian AusNut 1999 database (All Foods) Revision 17 and AusFoods (Brands) Revision 5 accessed through FoodWorks version 4.00.1158, 2005 (22) (Xyris Soft-ware, Brisbane, Queensland, Australia) to generate individual mean daily macro-and micronutrient intakes. FFQ’s were also analyzed using a modified version of the Food Works 210 nutrient calculation software (22), which combines the Australian food composition database (23) and the US Department of Agriculture–National Cancer Institute carotenoids food composition database (24) to generate mean daily carotenoid intakes.Total energy and percent energy contribution by fat, protein, and car-bohydrate were calculated. Total servings of fruit and vegetables were calculated by summing the weight of food items in the FFQ coded as fruit or vegetables and dividing by the serve size dictated in the national food selection guide, the Australian Guide to Healthy Eating (fruit 150 g, vegetables 75 g) (25). Australian Child and Adolescent Eating Survey FFQ frequency of response categories i.e., “less than once per week” were tallied as a percentage of total responses. Major food sources of carotenoids were classified as “commonly consumed” if it was reported to be consumed greater than or equal to twice per week.Phlebotomists collected blood samples in EDTA-coated tubes after an overnight fast. Samples were analyzed at an accredited pathology service (National Association of Testing Authorities, Australia) using Nutrition Validation Study (NVS)n = 47 Total children n = 93Outliers removed n = 3HIKCUPS n = 46HIKCUPS 6 month values counted asnew n = 46 Not enough blood for completeplasma carotenoid analysis n = 11Total sample n = 125Total children n = 136Total children n = 90Figure 1 Recruitment flow diagram for children included in analysis for validation of fasting plasma and dietary intake of carotenoids.

Page 3

164 VOLUME 17 NUMBER 1 | JANUARY 2009 | www.obesityjournal.orgarticlesepidemiologystandard automated techniques for total cholesterol and cholesterol frac-tions (low-density lipoprotein, high-density lipoprotein), triacylglycerol, insulin, and C-reactive protein. Plasma was separated from red blood cells by centrifugation and remaining samples were frozen within 2 h to −80 °C. Samples were thawed and reverse phase high-performance liquid chromatography (26) was used to determine plasma concentrations of α-carotene, β-carotene, lycopene, cryptoxanthin, and lutein. The caro-tenoid analysis was initiated by first isolating the carotenoids from the plasma sample. Ethanol: ethyl acetate (1:1) containing internal standards (cantaxanthin and BHA) were added to the sample and it was centrifuged (3,000 g, for 5 min at 4 °C) and the supernatant collected. This process was repeated three times, adding ethyl acetate twice, then hexane to the pellet. Ultra pure water was then added to the pooled supernatant and the mixture was vortexed and centrifuged. The supernatant was decanted, the solvents evaporated with nitrogen and the sample reconstituted in dichlo-romethane: methanol (1:2 vol/vol). Chromatography was performed on a hypersil ODS column (100 mm × 2.1 × 5 μm) with a flow rate of 0.3 ml/min. Carotenoids were analyzed using a mobile phase of acetonitrile: dichloromethane: methanol 0.05% ammonium acetate (85:10:5 vol/vol) and a diode array detector at 450 nm.statisticsDescriptive statistics were computed for all children. Outliers were removed based on reported dietary intakes of carotenoids beyond reasonable intake levels (>20,000 μg/day β-carotene, n = 3). An exclusion criterion was determined as at these higher levels, the amount of carotenoid rich food that would need to be consumed by a child daily was deemed not feasible. Descriptive statistics were computed for the whole sample of children. Six-month postinter-vention blood values of children from the overweight/obese sample (HIKCUPS) were included as separate values. As six-month samples for some subjects were included with their baseline values the use of multiple linear regression might lead to invalid conclusions due to the lack of independence of the data within a subject. So as a check of the effect of these repeated measurements a linear mixed model analysis was carried out using an unstructured covariance struc-ture to model the correlation of baseline and 6-month scores in the residuals. The regression coefficients from the mixed model were all within 10% of those from the multiple linear regression model and the P values for all terms good agreement with those from the regression analysis. The regression model results are valid then as they were very similar to those from the mixed model analysis. So the regression model was preferred as it provided a multiple correla-tion coefficient for the adjustment due to other factors. This allowed comparison with other studies and the whole model results could be compared with the unadjusted individual variable correlations.Comparative validity was assessed using Pearson correlation analy-ses between dietary intake (μg/day) and plasma concentration values (μmol/l) for each carotenoid. A one-way ANOVA was undertaken to determine differences between dietary and plasma levels for children from different weight groups (healthy, overweight, and obese). The level of significance was set at 0.05. All analyzes were completed using SPSS version 15.0 (SPSS, Chicago, IL).resultsComplete dietary and biochemical analysis data was available for 125 discrete samples. Table 1 reports characteristics of chil-dren by weight category. Parental reported dietary intakes of fruits, vegetables, and carotenoids did not differ significantly by weight group presented in Table 2. However, the plasma caro-tenoid concentrations significantly decreased with increased weight status, P < 0.05, except for lutein.Analysis of the frequency of responses of FFQ items that were major sources of carotenoids from FFQ showed that the five vegetables reported as commonly consumed in descend-ing order were carrots n = 101 children (81%), potatoes (n = 94, 75%), broccoli (n = 72, 58%), peas (n = 63, 50%) and let-tuce (n = 57, 46%). The least consumed vegetables were beans and lentils (n = 2, 1.4%), avocado (n = 5, 3.7%), spinach (n = 5, 3.7%), and cabbage (n = 6, 5.3%). Similarly for most com-monly consumed fruit: apple (n = 95, 76%), melon (n = 78, 63%) (watermelon, rockmelon, and honeydew), orange/grape-fruit (n = 66, 53%), banana (n = 65, 52%) and grapes/straw-berries/blueberries (n = 57, 46%). The least consumed were pineapple (n = 12, 9.5%) and mango (n = 17, 14%). Fruit juice was reported to be consumed by n = 93 children, 74% of study participants twice or more per week.A significant correlation was found between estimated dietary intake and plasma concentrations for α-carotene only r = 0.25, P <0.01. However, after adjustment for BMI, signifi-cant correlations were found for all carotenoids, except lutein, with coefficients ranging from 0.32 to 0.56 (Table 3). BMI was table 1 characteristics of study participants by weight groupDescriptive statisticsNormal rangeHealthy weight (n = 38)Overweight (n = 20)Obese (n = 35)Total (n = 93)Height (m)1.4 ± 0.132.5 ± 6.617.0 ± 1.658.9 ± 5.30.1 ± 0.79.8 ± 1.14.4 ± 0.62.5 ± 0.51.5 ± 0.30.7 ± 0.44.7 ± 4.02.9 ± 5.91.4 ± 0.141.6 ± 6.421.4 ± 1.268.1 ± 7.42.0 ± 0.38.8 ± 1.24.3 ± 0.62.6 ± 0.51.3 ± 0.30.9 ± 0.47.5 ± 6.22.0 ± 1.61.4 ± 0.150.0 ± 8.925.6 ± 2.379.8 ± 7.53.1 ± 0.58.4 ± 1.34.2 ± 0.72.5 ± 0.61.3 ± 0.31.0 ± 0.612.2 ± 9.84.1 ± 9.21.4 ± 0.141.2 ± 10.821.3 ± 4.268.9 ± 11.41.7 ± 1.49.0 ± 1.44.3 ± 0.62.5 ± 0.51.4 ± 0.30.9 ± 0.58.2 ± 7.93.2 ± 6.9Weight (kg)**BMI (kg/m2)**Waist circumference (cm)**BMI z score**Age (years)**Cholesterol (mmol/l)<4.9LDL (mmol/l)<4.0HDL (mmol/l)*1.0–2.2Triglycerides (mmol/l)<1.59Insulin (ml/l)**C-reactive protein (mmol/l)<3.0Values displayed as mean ± s.d. Weight group classified by BMI z score according to UK cut points as defined in ref. 17. Between weight groups, *P < 0.05, **P < 0.001.

166 VOLUME 17 NUMBER 1 | JANUARY 2009 | www.obesityjournal.orgarticlesepidemiologydIscussIonThe aim of this study was to examine the relative validity of parental report of children’s fruit and vegetable intake by FFQ using plasma carotenoids as a biomarker of intake. It was hypothesized that significant correlations would exist between reported levels of dietary carotenoids, as markers of fruit and vegetable intake, and plasma carotenoid concentrations. Moderate, significant correlations between intake and plasma levels were found for four of the five measured carotenoids, with β-carotene, α-carotene, and cryptoxanthin showing the strongest correlations, after adjustment for BMI. These caro-tenoids are predominantly found in fruit and vegetables with an orange appearance such as carrots and oranges, which were among the most commonly reported, consumed fruits and vegetables for study participants, in addition to fruit juice.Several validation studies have been reported in children. One previous study, using parental report of dietary intake by an 111 item FFQ for 97 children aged 6–10 years, showed a weak relationship between total dietary carotene and plasma carotene (r = 0.16, P < 0.05) (2). However dietary carotenoid intake was estimated as SI units of vitamin A and assumed to be representative of α-carotene, β-carotene, and cryptoxanthin intakes. Correlations with individual plasma carotenoids were not examined, possibly because detailed dietary carotenoid databases have only been available recently (27). A more recent report (13) in 285 adolescents of varying weight status showed significant correlations between self-reported dietary intake, using a 122-item FFQ, and nonfasting plasma carotenoid con-centrations, after adjustment for cholesterol, age, sex, race, energy intake, and BMI (cryptoxanthin (r = 0.38), α-carotene (r = 0.31), lutein (r = 0.25), and β-carotene (r = 0.15)].This study is the first to demonstrate stronger correlations for individual carotenoids after adjusting for BMI only, with r values ranging from 0.35 to 0.56. Due to the large intra- and interindividual variation in both dietary and plasma carotenoid values, the r values were expected at best to be modest, which is what we have found (28). In addition to parental misreport, this is most likely due to the many nondietary determinants of circulating carotenoid concentrations, including bioavail-ability, efficiency of absorption, metabolism, uptake and uti-lization, and the rate of catabolism (29). We have shown an important and significant relationship between dietary intake and fasting plasma levels of carotenoids in children aged 5–12 years, after adjustment for weight status.Limitations of this validation study include that the sample of children used were largely overweight and this group have been shown to have lower levels of plasma carotenoids (14,30). Thus, with the over-representation, it is likely that despite use of 6-month values, limited variation in plasma concentrations existed, which would have reduced the power of the study. The nonsignificant correlations seen in the overweight group was most likely due to lack of power. A further limitation is that the food database used to estimate dietary intake of carotenoids is not comprehensive, although updated in 2006. Correlates of dietary and plasma carotenoids may improve as food composition databases, internationally are expanded. Although FFQs have been used in large-scale studies, there are numerous sources of random and systematic error, as well as restrictions imposed by a fixed list of foods, portion size estimations, and the limited ability to differentiate between cooked and raw vegetables, which affects the bioavailability of carotenoids. However, despite limitations we used specific child portions sizes to estimate dietary intakes, and accounted for fruit and vegetable seasonality by adjusting FFQ response categories and collected data from participants throughout an entire calendar year.Significant correlations were not seen for lutein possibly because lutein-rich food sources were commonly consumed by children, i.e., spinach, and cabbage. Therefore a much larger sample would have been required for associations between die-tary and plasma lutein to reach statistical significance. There is also conflicting information about the role of β-carotene in inhibiting lutein absorption (31,32). High levels of dietary and plasma β-carotene reported in this sample of children may have confounded lutein levels.Plasma carotenoids may have shown stronger correlations with reported intake over a shorter time frame such as the pre-vious month. Longitudinal research suggests stability of chil-dren’s eating patterns over time (33,34) and that the pattern of carotenoids in human plasma reflects that of usual dietary intake (35). Dietary intake reported over a 6-month time frame should be not dissimilar from intake over a shorter, more recent time frame. The dietary and plasma carotenoid concentrations reported were from a one-off collection. Single collections may not accurately reflect true concentrations for either variable, but was the best available means to do so within financial con-straints. Data were collected over a 1-year time period, thus the amalgamation of plasma samples and reported dietary intakes are likely to reflect differences in seasonality.No significant correlation was found between reported mean daily servings of fruit and vegetables per day or mean daily caro-tenoid intake and plasma concentrations, except for α-carotene. This could be due to the large standard deviations on dietary estimates and biochemistry. However, after adjustment for weight status the correlations became statistically significant and were moderately strong. The FFQ used was sensitive in detecting differences in fruit and vegetable intake by plasma concentration. We recommend that both dietary intake data and plasma carotenoid concentrations be reported by weight status in future studies and that BMI or BMI z score should be included as a potential confounder in statistical analyzes.Individual mean plasma carotenoid concentrations in chil-dren found in this study are similar or greater than those found by others (13,36–38). Comparison of our data with a large nationally representative sample of US children from the National Health and Nutrition Examination Survey (NHANES III) (14), where plasma β-carotene was measured, shows that our results are most similar to those of the nonobese children taking vitamin supplements in the US cohort. In this study, using an FFQ, all parents reported higher intakes of both fruit and vegetables compared to NHANES III (14). They also met Australian recommendations of two servings of fruit